We follow [1]} and [2]} to warm-start our policy training in the first \(N_{\mathrm {warm}}\)  epochs.
In this step, the policy is optimized w.r.t. the generator loss \({\mathcal {L}}_g({\mathbf {\phi }})\)  only, i.e., \(\arg \min _{\mathbf {\phi }}{\mathcal {L}}_g({\mathbf {\phi }})\) .
The learning rate \(\eta _{\mathbf {\phi }}\)  in the warm-start step is the same as the normal training step.
